title: Analyzing Beijing's in-use vehicle emissions test results using logistic regression.
abstract: A logistic regression model was built using vehicle emissions test data collected in 2003 for 129 604 motor vehicles in Beijing. The regression model uses vehicle model, model year, inspection station, ownership, and vehicle registration area as covariates to predict the probability that a vehicle fails an annual emissions test on the first try. Vehicle model is the most influential predictor variable: some vehicle models are much more likely to fail in emissions tests than an "average" vehicle. Five out of 14 vehicle models that performed the worst (out of a total of 52 models) were manufactured by foreign companies or by their joint ventures with Chinese enterprises. These 14 vehicle model types may have failed at relatively high rates because of design and manufacturing deficiencies, and such deficiencies cannot be easily detected and corrected without further efforts, such as programs for in-use surveillance and vehicle recall.
authors.affiliation: Department of Civil and Environmental Engineering, Stanford University, Jerry Yang & Akiko Yamazaki Environment & Energy Building, Room 249, Stanford, California 94305-4020, USA.
authors.forename: Cheng
authors.initials: C
authors.lastname: Chang
authors.forename: Leonard
authors.initials: L
authors.lastname: Ortolano
mesh_terms: D002681:China; D016001:Confidence Intervals; D004777:Environment; D016015:Logistic Models; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D001335:Vehicle Emissions